In this paper, we introduce a complete system for autonomous flight of quadrotors in dynamic environments with onboard sensing. Extended from existing work, we develop an occlusion-aware dynamic perception method based on depth images, which classifies obstacles as dynamic and static. For representing generic dynamic environment, we model dynamic objects with moving ellipsoids and fuse static ones into an occupancy grid map. To achieve dynamic avoidance, we design a planning method composed of modified kinodynamic path searching and gradient-based optimization. The method leverages manually constructed gradients without maintaining a signed distance field (SDF), making the planning procedure finished in milliseconds. We integrate the above methods into a customized quadrotor system and thoroughly test it in realworld experiments, verifying its effective collision avoidance in dynamic environments.
翻译:在本文中,我们引入了一种完整的系统,用于在动态环境中以机载感测方式自发飞行。我们从现有工作出发,根据深度图像开发一种封闭感知动态方法,将障碍归类为动态和静态。为了代表通用动态环境,我们用移动的椭球体模拟动态物体,并将静态物体结合到占用网格图中。为了实现动态避免,我们设计了一种规划方法,由经修改的动态动力路径搜索和梯度优化组成。该方法利用人工构造的梯度,而没有保持签名的距离场(SDF),使规划程序在毫秒内完成。我们将上述方法纳入一个定制的二次驱动系统,并在现实世界实验中彻底测试它,核查其在动态环境中有效避免碰撞的情况。